Dissecting autoimmune cellular and molecular networks in vitiligo

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Abstract: Vitiligo is a disfiguring organ-specific autoimmune disease that affects 1% of the world population, including 3 million individuals in the U.S. alone. There are no FDA-approved medical treatments for vitiligo, and current first-line therapies have marginal efficacy. What controls the spreading of vitiligo? Why is its rate of progression different from patient to patient? These are poorly understood problems. In this project we seek to provide the molecular mechanism that explains these fundamental questions. Our paradigm-shifting hypothesis is that the balance between inflammatory and tolerogenic pathways directly within the skin determines the location of lesions and their rate of progression. Thus, local changes in skin are at the core of vitiligo progression. We propose to test and expand our hypothesis using technological advances that only now make this possible. First, we developed a suction blistering method that provides robust sampling of tissue-infiltrating immune cells without confounding inflammation from blistering itself. Second, we have established a single cell RNA-Seq (scRNA-Seq) methodology and have successfully applied it to blister fluid samples. Third, we propose to develop gridRNA-Seq where we use laser microdissection of incisional biopsies of vitiligo lesions into grids of small cubes of tissue containing 10-20 cells each, representing the three dimensional tissue structure. We then use a low input RNA-Seq protocol to profile the expression level of each cube. The combination of these three technologies put us in a unique position to fundamentally change our approach to study vitiligo. With scRNA-Seq we will find the cell types present within vitiligo lesions and compile gene expression signatures for each of these cells. Using ligand/receptor analysis we will build cell signaling networks to support models of disease progression. By comparing lesional with nonlesional blister samples we will identify aberrant signaling in vitiligo, and in particular how tolerogenic and inflammatory signals differ between lesions and nonlesional skin. Although powerful, scRNA-Seq sequences a cell mixture, and the origin of the cells is lost. To address this limitation, we will develop gridRNA-Seq to profile a “block” of 10-20 cells from a known, demarcated region in the skin. Each block in the grid has a well-characterized phenotype as established by the histological appearance at the location of the block. By integrating cell type specific signatures with bulk RNA-Seq data from gridRNA-Seq we propose to “deconvolve” this mixed expression data to estimate the cell composition of each block as well as its expression. We will then use this information to correlate specific expression and cell type composition changes with disease phenotype. Taken together, our data will directly assess the exact tolerogenic and inflammatory balance of individual cells and its association with disease phenotype and hence both test our original hypothesis as well as provide an unbiased view of vitiligo pathogenesis at the individual cell level.